25 citations · 58 across the 23 of their papers we have counts for
7 papers · 1 filter
Adversarial Environment Design via Regret-Guided Diffusion Models
Hojun Chung, Junseo Lee, Minsoo Kim +2
Training agents that are robust to environmental changes remains a significant challenge in deep reinforcement learning (RL). Unsupervised environment design (UED) has recently eme…
RNR-Nav: A Real-World Visual Navigation System Using Renderable Neural Radiance Maps
Minsoo Kim, Obin Kwon, Howoong Jun +1
We propose a novel visual localization and navigation framework for real-world environments directly integrating observed visual information into the bird-eye-view map. While the r…
Semantic Environment Atlas for Object-Goal Navigation
Nuri Kim, Jeongho Park, Mineui Hong +1
In this paper, we introduce the Semantic Environment Atlas (SEA), a novel mapping approach designed to enhance visual navigation capabilities of embodied agents. The SEA utilizes s…
Stage-Wise Reward Shaping for Acrobatic Robots: A Constrained Multi-Objective Reinforcement Learning Approach
Dohyeong Kim, Hyeokjin Kwon, Junseok Kim +2
As the complexity of tasks addressed through reinforcement learning (RL) increases, the definition of reward functions also has become highly complicated. We introduce an RL method…
Safe CoR: A Dual-Expert Approach to Integrating Imitation Learning and Safe Reinforcement Learning Using Constraint Rewards
Hyeokjin Kwon, Gunmin Lee, Junseo Lee +1
In the realm of autonomous agents, ensuring safety and reliability in complex and dynamic environments remains a paramount challenge. Safe reinforcement learning addresses these co…
Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees
Dohyeong Kim, Taehyun Cho, Seungyub Han +3
The field of risk-constrained reinforcement learning (RCRL) has been developed to effectively reduce the likelihood of worst-case scenarios by explicitly handling risk-measure-base…
Conflict-Averse Gradient Aggregation for Constrained Multi-Objective Reinforcement Learning
Dohyeong Kim, Mineui Hong, Jeongho Park +1
In many real-world applications, a reinforcement learning (RL) agent should consider multiple objectives and adhere to safety guidelines. To address these considerations, we propos…